[SurgeryNet] Epilepsy surgery induced brain network changes: relation to patient outcomes

癫痫是一种严重的神经系统疾病，影响着英国60多万患者。患者癫痫发作可导致意识丧失、抽搐，甚至增加猝死的风险。药物只对大约三分之二的患者有效，这促使人们需要替代疗法。对许多人来说，脑外科手术是一个严肃的选择，切除被认为会导致癫痫发作的大脑部分。然而，即使在侵入性切除脑组织后，癫痫发作仍会在多达一半的患者中复发。在过去的几十年里，我们对如何研究大脑的思考发生了一场革命。计算和方法学的进步使我们能够将大脑视为一个相互作用区域的复杂网络，而癫痫则是区域内部和区域之间异常相互作用的紊乱。考虑到大脑网络可以用许多不同的方法来测量，考虑到癫痫手术可以被认为是对网络的改变，很快就会发现这一挑战是极其复杂的。简而言之，我们需要利用衍生脑网络的复杂性来改善癫痫患者的手术治疗，而不是被它们所阻碍。在这项研究中，我将开发和使用先进的计算技术来分析从500多名癫痫患者那里获得的术前数据。我将使用这些技术来生成个性化的大脑网络，然后使用计算机模型来预测患者的结果。由于这组500名患者已经接受了手术，我将把预测结果与患者的实际结果进行比较。在奖学金的第二阶段，我将把这些模型应用于世界各地医院的新患者数据，以测试这些预测是否可靠。最后，我计划进行前瞻性分析，以评估患者在现实临床环境中的获益。如果成功，这项研究将会产生预测性和机械性模型，为手术的临床决策提供信息。

Epilepsy is a serious neurological condition affecting over 600,000 patients in the UK. Patients have seizures which can result in loss of consciousness, convulsions, and even increased risk of sudden death. Drugs are only effective for around two thirds of patients, motivating the need for alternative treatments. Brain surgery, where the part of the brain thought to be causing seizures is removed is a serious option for many. However, even after the invasive removal of brain tissue, seizures still recur in up to half of patients. The past decades have seen a revolution in our thinking of how to study the brain. Computational, and methodological advances have allowed us to think of the brain as a complex network of interacting regions - and epilepsy as a disorder of abnormal interactions within and between regions. Given that brain networks can be measured in many different ways, and given that epilepsy surgery can be thought of as a change to a network, it soon becomes apparent that this challenge is extremely complex. In short, we need new ways to improve the surgical treatment of patients with epilepsy by leveraging the complexity of the derived brain networks, rather than being hampered by them.In this fellowship I will develop and use advanced computational techniques to analyse pre-surgery data acquired from over 500 patients with epilepsy. I will use these techniques to generate personalised brain networks, then use computer models to predict patient outcomes. Since this group of >500 patients already underwent surgery I will compare the predictions to the actual patient outcomes. In the second phase of the fellowship I will apply these models to new patient data from hospitals around the world to test if the predictions are robust. Finally, I plan to conduct prospective analysis to evaluate patient benefit in real-life clinical settings. If successful this fellowship will lead to predictive and mechanistic models to inform clinical decision making for surgery.